Search Results for author: Yimeng Chen

Found 17 papers, 1 papers with code

HI-CMLM: Improve CMLM with Hybrid Decoder Input

no code implementations INLG (ACL) 2021 Minghan Wang, Guo Jiaxin, Yuxia Wang, Yimeng Chen, Su Chang, Daimeng Wei, Min Zhang, Shimin Tao, Hao Yang

Mask-predict CMLM (Ghazvininejad et al., 2019) has achieved stunning performance among non-autoregressive NMT models, but we find that the mechanism of predicting all of the target words only depending on the hidden state of [MASK] is not effective and efficient in initial iterations of refinement, resulting in ungrammatical repetitions and slow convergence.

NMT Translation

Make the Blind Translator See The World: A Novel Transfer Learning Solution for Multimodal Machine Translation

no code implementations MTSummit 2021 Minghan Wang, Jiaxin Guo, Yimeng Chen, Chang Su, Min Zhang, Shimin Tao, Hao Yang

Based on large-scale pretrained networks and the liability to be easily overfitting with limited labelled training data of multimodal translation (MMT) is a critical issue in MMT.

Multimodal Machine Translation NMT +2

Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization

no code implementations5 Jun 2023 Yimeng Chen, Tianyang Hu, Fengwei Zhou, Zhenguo Li, ZhiMing Ma

The proliferation of pretrained models, as a result of advancements in pretraining techniques, has led to the emergence of a vast zoo of publicly available models.

Domain Generalization Out-of-Distribution Generalization

PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction

no code implementations18 Oct 2022 Yuancheng Sun, Yimeng Chen, Weizhi Ma, Wenhao Huang, Kang Liu, ZhiMing Ma, Wei-Ying Ma, Yanyan Lan

In our implementation, we adopt both the state-of-the-art molecule embedding models under the supervised learning paradigm and the pretraining paradigm as the molecule representation module of PEMP, respectively.

Drug Discovery Molecular Property Prediction +2

When Does Group Invariant Learning Survive Spurious Correlations?

1 code implementation29 Jun 2022 Yimeng Chen, Ruibin Xiong, ZhiMing Ma, Yanyan Lan

Motivated by this, we design a new group invariant learning method, which constructs groups with statistical independence tests, and reweights samples by group label proportion to meet the criteria.

Out-of-Distribution Generalization

Self-Distillation Mixup Training for Non-autoregressive Neural Machine Translation

no code implementations22 Dec 2021 Jiaxin Guo, Minghan Wang, Daimeng Wei, Hengchao Shang, Yuxia Wang, Zongyao Li, Zhengzhe Yu, Zhanglin Wu, Yimeng Chen, Chang Su, Min Zhang, Lizhi Lei, Shimin Tao, Hao Yang

An effective training strategy to improve the performance of AT models is Self-Distillation Mixup (SDM) Training, which pre-trains a model on raw data, generates distilled data by the pre-trained model itself and finally re-trains a model on the combination of raw data and distilled data.

Knowledge Distillation Machine Translation +1

Joint-training on Symbiosis Networks for Deep Nueral Machine Translation models

no code implementations22 Dec 2021 Zhengzhe Yu, Jiaxin Guo, Minghan Wang, Daimeng Wei, Hengchao Shang, Zongyao Li, Zhanglin Wu, Yuxia Wang, Yimeng Chen, Chang Su, Min Zhang, Lizhi Lei, Shimin Tao, Hao Yang

Deep encoders have been proven to be effective in improving neural machine translation (NMT) systems, but it reaches the upper bound of translation quality when the number of encoder layers exceeds 18.

Machine Translation NMT +1

Uncertainty Calibration for Ensemble-Based Debiasing Methods

no code implementations NeurIPS 2021 Ruibin Xiong, Yimeng Chen, Liang Pang, Xueqi Chen, Yanyan Lan

Ensemble-based debiasing methods have been shown effective in mitigating the reliance of classifiers on specific dataset bias, by exploiting the output of a bias-only model to adjust the learning target.

Fact Verification

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